Many emerging applications need continuous querying over uncertain event streams, mostly for online monitoring. These streaming uncertain events may come from radars, sensors, or even software hooks. The uncertainty is usually due to measurement errors, inherent ambiguities and privacy preserving reasons. To cover new requirements, we have designed and implemented a new system called Probabilistic Data Stream Management System (PDSMS) in Ref. 1. PDSMS is a data processing engine which runs continuous queries over probabilistic streams. However, lack of a semantics for probabilistic databases which supports continuous distributions prevented us from having a strong foundation for our query operators. It also precludes us from proving consistency and correctness of query operations especially after optimization and adaption. In fact, in the probabilistic database literature, there is no semantics available which covers continuous distributions. This limitation is very restrictive as in real-world, uncertainty is usually modeled by continuous distributions. In this paper, after presenting a basic probabilistic data model for PDSMS, we focus on querying and formally present the first semantics for probabilistic query operations which supports continuous distributions as well as discrete ones. Using this new semantics, we define our query operators (e.g. select, project, and join) formally without ambiguity and compatible with operators in relational algebra. Thus, we can leverage many transformation rules in relational algebra as well. This new semantics allows us to have different strictness levels and consistency between operators. We also proved many strictness theorems about different alternatives for query operators.

Inherent imprecision of data in many applications motivates us to support uncertainty as a first-class concept. Data stream and probabilistic data have been recently considered noticeably in isolation. However, there are many applications including sensor data management systems and object monitoring systems which need both issues in tandem. Our main contribution is designing a probabilistic data stream management system, called Sarcheshmeh, for continuous querying over probabilistic data streams. Sarcheshmeh supports uncertainty from input data to final query results. In this paper, after reviewing requirements and applications of probabilistic data streams, we present our new data model for probabilistic data streams and define our main logical operators formally. Then, we present query language and physical operators. In addition, we introduce the architecture of Sarcheshmeh and also describe some major challenges like memory management and our floating precision mechanism toward designing a more robust system. Finally, we report evaluation of our system and the effect of floating precision on the tradeoff between accuracy and efficiency.

Nowadays XML is widely used by many applications. XML is not only used for exchanging data among the applications but also is used to store the semi-structured data. Managing huge data stored in XML documents, emphasizes the need for XML databases. One of the most important features of a database in a multi-user environment is its concurrency control mechanism. We discuss that many concurrency control protocols found in the literature either provide a restricted concurrency level which is less than what logically could be or provide a high level of concurrency which may lead to some defects. To overcome these problems, we offer a novel locking protocol with some rich locks named "XML Path Locking by Child Consideration" (XPLC). In our approach unlike the previous ones, we consider the child of the node which we want to lock. Also our locks have different granularities according to their types.

Multiple-query optimizers create materialized view from multi queries, to execute common operations of different queries not more than once. This technique is useful especially in subsumption and overlapping queries with common parts. Moreover, caching intermediate or final results of previous queries and using them for future ones can noticeably improve response time. Using intelligent decision making mechanism to cache the desired results is a major consideration to achieve this goal. In this paper, we present a novel method for multiple-query optimization which efficiently caches intermediate results of materialized views of received queries in a sliding window. The method is independent of database models. Its performance in term of response time and cache hit ratio as well as reduction in data retrieval rate is evaluated via simulation.

Previous researches, in the filed of XML databases, have been done to evaluate XML queries with AND-branches. However, as far as we know, very little work has examined the efficient processing of XML queries with NOT-predicates. Also these methods have to process all of the query nodes in the document when dealing with queries with NOT-branch. In this paper, with some modification in TJFast method, we propose a new manner for answering to various NOT-queries. This method processes nodes efficiently, in a way that in the ideal state, we obtain part of the answer after the process of each node, and we don't have any unreasonable processing of each node.

Workflow systems are receiving increased attention as they intend to facilitate the operations of enterprises by coordinating and streamlining business activities. The need for automated support and operational models that allow workflow applications to coordinate units of work across multiple servers-according to business defined rules and routes-is becoming critical for the proper management of such activities. In this paper, we describe a Transaction-Oriented Workflow Environment (TOWE) for programming workflow activities. The novelty of our approach resides in the proposed unified abstraction, class libraries, to support workflow activities. The fundamental concept used in the TOWE system is based on the symbiosis of object-oriented programming and interprocess communication concepts. In TOWE, the concurrency abstractions are represented by process objects, active objects acting as processes, which involve asynchronous, location-independent, and application specific process invocations.

Providing a general purpose programming environment that supports the definition of, and exercises control over, the flow of execution of long-running activities is highly beneficial for a variety of client/server distributed data-intensive applications. In this paper, we present a Transaction-Oriented Work-Flow Environment (TOWE) for the programming of long-lived activities through a set of class libraries. The TOWE is based on an amalgamation of object-oriented programming with distributed interprocess communication concepts. The concurrency abstractions provided by TOWE are objects, acting like processes, and involve an asynchronous, location-independent, mode of process invocation coupled with data-driven synchronization of processes.

A transaction management model for distributed information systems that utilizes actor-like objects (TrActor) which deviate from conventional actors in their objects types, messages types, and mail queue is described. The objects are used in conjunction with distributed blackboarding techniques for addressing the process of reasoning about the need for cooperation and negotiation between the disparate information sources in a distributed information network. A variant of the nested transaction model is proposed as an information processing framework for the management model. A language for this framework is also proposed, and the feasibility of this approach is demonstrated by means of a comprehensive example.>